CN114389756A - Uplink MIMO detection method based on grouping ML detection and parallel iteration interference cancellation - Google Patents
Uplink MIMO detection method based on grouping ML detection and parallel iteration interference cancellation Download PDFInfo
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Abstract
The invention relates to an uplink MIMO detection method based on grouping Maximum Likelihood (ML) detection and parallel iterative interference cancellation, which comprises two main parts: grouped ML detectors and parallel iterative interference cancellers. When each iteration starts, according to the detection result output by the last iteration, the influence of other groups of users on a group to be detected is counteracted through parallel interference cancellation, and then ML detection is carried out on the data stream in the group to be detected. The detection method reduces the core detection complexity through the grouped ML detection and the simplified sorting algorithm, and ensures the excellent detection performance through iterative parallel interference cancellation. Under a receiving scene with high diversity degree, the method has the performance of a near-optimal detector and global ML detection.
Description
Technical Field
The invention relates to an uplink MIMO (multiple-antenna transmission) detection method based on grouped ML (maximum likelihood) detection and parallel iterative interference cancellation, belonging to the technical field of wireless mobile communication.
Background
A massive MIMO (Multiple-Input Multiple-Output) technology is first proposed in the third generation mobile wireless communication network research, which aims to improve the spectrum efficiency and the link reliability by using Multiple transmitting and receiving antennas. Therefore, massive MIMO technology also becomes a key technology for satisfying users' demand for higher quality of service (Qos) in the 5 th generation mobile communication system. However, there is interference between antennas, so to further improve the communication quality, an additional MIMO detection algorithm needs to be deployed in the base station to cancel the interference between antennas.
In recent years, the number of users in wireless mobile networks has increased dramatically, and the interaction between base stations and users has reached the order of magnitude of bytes in some small cities. Since 2015 to 2021, the total data interaction amount between global base stations and users increases exponentially, and the increase in the number of users puts demands on wireless mobile network service providers for higher spectral efficiency, higher energy efficiency, higher transmission rate and better mobility. To meet these demands, a solution is to use massive MIMO technology, but as the antenna size increases and the uplink allowed to access increases, the complexity of the MIMO detector increases rapidly, and to solve this problem, a high-performance low-complexity detection algorithm needs to be deployed correspondingly
In fact, since the last 50 years of the advent of MIMO technology, the research of MIMO detectors has been of great interest in the academic world. On one hand, along with the improvement of chip technology, the enhancement of computer computing power enables the deployment of high-performance MIMO detection algorithm with higher complexity to be possible. On the other hand, with the deep research of MIMO technology in the scientific community, a large number of high-performance algorithms capable of being physically realized are proposed. MIMO detection algorithms can be roughly classified into linear detection algorithms and nonlinear detection algorithms, wherein the mainstream and commonly used algorithms are MMSE detection algorithms based on linear optimal derivation and spherical decoding (FCSD) algorithms based on approximately maximum likelihood and fixed complexity. However, compared with the performance of a nonlinear detection algorithm, the detection performance of the MMSE algorithm is poor, and the implementation complexity of the FCSD algorithm with high detection performance is high. And, with the increase of the sending data stream, the complexity of both algorithms is obviously improved. In terms of detection performance, the performance of the FCSD detection algorithm has a great relationship with the data stream ordering accuracy, but the algorithm complexity of a high-performance ordering algorithm such as an SNR criterion ordering method is also high, and compared with the MMSE algorithm, although the complexity is slightly low, the performance is obviously attenuated, and the algorithm complexity of the two algorithms is significantly increased along with the increase of the number of the transmitted data streams.
In summary, in the current research on the large-scale MIMO detection algorithm, on one hand, the contradiction between high performance and low complexity needs to be solved, and on the other hand, the problem of complexity improvement caused by a large increase in the transmission data stream needs to be solved.
Disclosure of Invention
The invention aims to solve the technical problem of how to provide a high-performance uplink MIMO detection scheme with moderate complexity.
The invention adopts the following technical scheme for solving the technical problems: the invention designs an uplink MIMO detection method based on grouped ML detection and parallel iterative interference cancellation, which is used for carrying out real-time detection on uplink centralized single-user or multi-user MIMO received signals and executing the following steps on MIMO signals to be detected obtained in real time:
step A, receiving an MIMO signal to be detected, performing grouped ML detection on the MIMO signal to be detected to obtain an initial value of parallel iterative interference cancellation, setting a maximum iteration number T, and then entering step B, wherein a MIMO receiving signal y to be detected can be expressed as:
wherein HiChannel, x, representing the ith useriThe base station can express the transmission data flow of the ith user, n is additive white noise, K is the total number of users, and the base station can express the form of y being Hx + n in a concise manner according to the form of a block matrix in the centralized reception detection
And B, executing parallel interference cancellation, namely subtracting the interference of the data stream to be detected of other group data streams, and then detecting the data stream in the group to be detected by using a grouped ML detection method, wherein the processes are executed in parallel by taking the group as a unit and are regarded as one iteration. The output of the iteration detection is used as the initial value of the parallel interference cancellation of the next iteration.
And C, skipping to execute the step B, adding 1 to the iteration times until the iteration times reach a preset value, and outputting a final detection result by the parallel iteration interference cancellation detector.
As a key technical solution of the present invention, the packet ML detection in step a includes the following steps:
step A1: grouping the sending data stream and the channel matrix, wherein the number L of the data streams in the group is more than or equal to 2 and less than or equal to 4. Setting the number of transmitting antennas to be NtThen, in total, it can be divided into: p is NtAnd the group is divided into continuous uniform groups.
Step A2: and B, according to the grouping result of the step A1, grouping and sorting the received data, and according to the sorting result, adjusting the column sequence of the corresponding channel matrix to be:
step A3: for the matrix adjusted in the sequence
Is subjected to QR decomposition to obtain
Obtaining the effective part y of the received vector according to the QR decomposition resulteff＝QHy, grouping the matrix R according to the grouping result of step a1 and the p-th grouping can be expressed as: rp＝[r(p-1)*L+1,r(p-1)*L+2,…,r(p-1)*L+L]，riIs the ith column vector of matrix R.
Step A4: using maximum likelihood detection in the group, the detection process of the p group of the t iteration is
CLWhich represents the space formed by all possible transmitted symbol vectors generated when the modulation symbols belonging to modulation space C are transmitted in parallel on the L data streams. After the detection is completed, the interference of the p-th group to other data streams is cancelled, and the cancellation process can be represented as:
step A5: and (4) carrying out detection in the sequence of P, P-1, … and 1, repeating the step A4 until all data streams are detected, and outputting a final detection result.
As a preferred technical solution of the present invention, the packet ordering method in step a2 includes the following steps:
step A2.1: by HoriginAfter the obtained channel matrix is stored as H, the channel matrix H is inverted to obtain
NtIn order to transmit the number of antennas,
for the channel inverse matrix H+The ith row vector.
Step A2.2: and respectively calculating the total amplification factor Score of the data streams in the group to be ordered according to the grouping result, wherein the calculation method comprises the following steps:
the group that should be currently detected is
Step A2.3: deletion of p (th)curThe corresponding column vector of the group data stream in the channel matrix can be expressed as:
and inverting the deleted matrix to obtain a new H+. In the invention, the subtraction operation (- { }) of the matrix is defined as deleting the corresponding column in the { }.
Step A2.4: repeat steps a2.2, a2.3 until all groups are sorted.
Step A2.5: adjusting the original channel matrix H according to the sorting resultoriginSuch that the top-ranked group is first detected
As a key technical solution of the present invention, the iterative interference cancellation method in step B includes the following steps:
step B1: the packet ML algorithm is used as the initial value of detection
The superscript indicates the number of iterations.
Step B2: in parallel, the interference cancellation is performed in units of groups, and the interference cancellation process for the p-th group of data can be expressed as:
wherein
The estimated value of the ith data stream is obtained when T is 0, … and T-1, which can be specifically expressed as
Step B3: in parallel, ML detection is performed in units of groups, and the detection result of the p-th group of data can be expressed as:
step B4: the interference cancellation and detection formed by B2 and B3 is an iteration, and T ═ T +1 is repeatedly executed until a preset iteration time T ≦ T, which is usually T ≦ 5.
Compared with the prior art, the uplink MIMO detection method based on the grouped ML detection and the parallel iterative interference cancellation has the following technical effects by adopting the technical scheme:
the detection complexity is reduced compared with the existing high-performance approximate MLD decoding detector (such as a fixed complexity sphere detection method (FCSD) based on ordering) through grouping ML detection and a simplified grouping ordering method, and the detection accuracy is ensured to have better performance compared with an optimal linear detection method (MMSE) through adopting an intra-group ML detection and a parallel iterative interference cancellation method.
Drawings
FIG. 1 is a flow chart of the detection of the design method of the present invention;
FIG. 2 is a schematic diagram of tree search for packet ML detection when a data stream is packetized in the design method 2 according to the present invention;
FIG. 3 shows a simulation embodiment one: a detection performance diagram corresponding to a receiving antenna for transmitting data stream 32 under an independent complex Gaussian channel 16;
FIG. 4 is a simulation example two: the associated channel 16 transmits a detection performance map corresponding to the data stream 32 receiving antenna.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings.
The invention provides an uplink MIMO detection method based on grouped ML detection and parallel interference cancellation, which mainly comprises two main parts, namely a grouped ML detection algorithm and parallel iterative interference cancellation, wherein a detection result of the grouped ML algorithm can be used as an initial value of the parallel interference cancellation method, and the ML detection method is adopted for group internal elements after the parallel interference cancellation is finished. As a preferable scheme of the sorting part of the grouped ML detection method, the invention provides a scheme for sorting by group unit based on SNR criterion, which has lower complexity because of less inversion times in the sorting process compared with the sorting scheme based on the SNR criterion and has better performance compared with the non-sorting grouped ML detection scheme. In summary, the present invention provides a novel uplink MIMO detection scheme, which can take into account both computational complexity and detection performance, has better detection performance compared to a linear detector, has lower complexity compared to a high performance MLD detector, and especially has performance close to an optimal ML detector in a MIMO system with a certain diversity order. The above-described important techniques included in the present invention will be described in further detail below.
Firstly, a MIMO system model according to which the invention is based in the simulation process is given. In the centralized uplink multi-user MIMO system, if it is assumed that multiple users simultaneously transmit signals to the base station, the received signal at the base station side may be represented as:
wherein HiChannel, x, representing the ith useriThe base station may simply express that y is Hx + n in a block matrix format. Channel matrix H for each useriThe present invention assumes a flat rayleigh fading channel that is a correlated or independent complex gaussian. Each element in a complex gaussian flat rayleigh fading channel, i.e. the channel matrix, satisfies a zero mean gaussian distribution, i.e.:
here, the
Representing the kronecker product, it is assumed in the MIMO system studied by the present invention that each user is full of streamsL for transmitting, the number of user transmitting antennas being equal to the number of transmitting data streamskAnd (4) showing. The correlation channel, i.e. the complex gaussian channel with added correlation process, can be mathematically expressed as:
wherein
RtAnd RrThe correlation matrix R is a correlation matrix of a sending end and a receiving end, the dereferencing methods of elements of the correlation matrix R and the receiving end are similar, and the correlation matrix R is a correlation matrix of a sending end and a receiving endt(Rr) The value of the ith row and the jth column element in (1) is as follows:
the difference lies in the correlation matrix RrDimension of (A) is Nr×NrThe correlation matrix RtDimension of (d) is L. ρ in the equation (4) is referred to as a correlation coefficient satisfying 0 < ρ < 1, and the larger the correlation coefficient setting, the stronger the correlation of the generated correlation channel matrix.
For an uplink multi-user MIMO system, the correlation between data streams from the same user is often high, and the correlation between data streams from different users is low. According to the conclusion, the grouping algorithm described in the invention divides the data streams of the same user into one group as much as possible. I.e. the data stream L in each group satisfies:
the corresponding total packet number P then satisfies:
Nt＝K×Lkindicating the total number of transmission streams, and assuming that the number of transmission streams is equal to the number of transmission antennas and therefore equal to the total number of transmission antennas in the system model, L in equation (2)maxIn order to manually set parameters, the performance and the operation complexity need to be comprehensively considered during setting, and the research of the invention finds that the two aspects can be considered when the value is generally set to 4. For the detection algorithm, the higher the correlation between data streams, the greater the difficulty of detection accuracy. For example, for a linear MMSE detector, the detection performance (the difference between the detector output data stream and the true data stream) increases significantly as the correlation between the data streams increases. Thus, the present invention employs the best-performing ML detection scheme for highly correlated data streams within the same group, i.e., the
The grouping ML algorithm provided by the invention uses a serial interference cancellation method among groups, the serial interference cancellation method and a spherical decoding algorithm such as an FCSD algorithm are sensitive to a detection sequence, and the reasonable detection sequence can improve the detection accuracy of the method. However, the sorting algorithm for obtaining the detection order according to the optimal detection criterion (such as SNR criterion) consumes a lot of computing resources in determining the detection order. In the process of determining the detection sequence due to numerical calculation, each time the current data stream to be detected is output, the data stream needs to be deleted after the column corresponding to the channel matrix is deleted to perform inversion again on the channel matrix H, the operation amount is huge, and particularly the algorithm complexity Θ and oc N in a large-scale MIMO system is larget 4However, the complexity Θ oc of the packet sorting algorithm employed in the present packet ML algorithm is P × Nt 3In N attThe algorithm complexity can be significantly reduced when larger.
Because the detection accuracy is reduced by adopting a simplified sequencing algorithm, a stage of parallel iterative interference cancellation is additionally arranged in the invention. The parallel iterative interference cancellation method performs interference cancellation using the detection result of the above-described packet ML algorithm as an initial value, and uses the result of packet ordering. As an optimal method, the ML detection method is continuously adopted in the group during the iterative interference cancellation, and the detection performance is improved under the condition of not obviously improving the algorithm complexity. The sorting complexity mentioned above can be converted to an operation generated by parallel iterative interference cancellation to a certain extent, namely a simpler sorting algorithm is used in sorting but the iteration times are increased in iterative interference cancellation.
To sum up, the present invention designs an uplink MIMO detection method based on packet ML detection and parallel interference cancellation, which is used for real-time detection of an uplink MIMO signal to be detected in an uplink massive MIMO scene, and in specific practical applications, for the uplink MIMO signal to be detected obtained in real time, the following steps are performed:
step A, receiving an MIMO signal to be detected, performing grouped ML detection on the MIMO signal to be detected to obtain an initial value of parallel iterative interference cancellation, setting a maximum iteration number T, and then entering step B, wherein a MIMO receiving signal y to be detected can be expressed as:
y＝H1x1+H2x2+…+HKxK+n
Expression of jie in the form of y ═ Hx + n
And B, executing parallel interference cancellation, namely subtracting the interference of the data stream to be detected of other group data streams, and then detecting the data stream in the group to be detected by using a grouped ML detection method, wherein the processes are executed in parallel by taking the group as a unit and are regarded as one iteration. The output of the iteration detection is used as the initial value of the parallel interference cancellation of the next iteration.
And C, skipping to execute the step B, adding 1 to the iteration times until the iteration times reach a preset value, and outputting a final detection result by the parallel iteration interference cancellation detector.
As a key technical solution of the present invention, the packet ML detection in step a includes the following steps:
step A1: grouping the sending data stream and the channel matrix, wherein the number L of the data streams in the group is more than or equal to 2 and less than or equal to 4. Setting the number of transmitting antennas to be NtThen, in total, it can be divided into: p is NtAnd the group is divided into continuous uniform groups.
Step A2: and B, according to the grouping result of the step A1, grouping and sorting the received data, and according to the sorting result, adjusting the column sequence of the corresponding channel matrix to be:
step A3: for the matrix adjusted in the sequence
Is subjected to QR decomposition to obtain
Obtaining the effective part y of the received vector according to the QR decomposition resulteff＝QHy, grouping the matrix R according to the grouping result of step a1 and the p-th grouping can be expressed as: rp＝[r(p-1)*L+1,r(p-1)*L+2,…,r(p-1)*L+L]，riIs the ith column vector of matrix R.
Step A4: using maximum likelihood detection in the group, the detection process of the p group of the t iteration is
CLRepresents all possible transmission symbols generated when modulation symbols belonging to the modulation space C are transmitted in parallel on L data streamsMeasure the space formed. After the detection is completed, the interference of the p-th group to other data streams is cancelled, and the cancellation process can be represented as:
step A5: and (4) carrying out detection in the sequence of P, P-1, … and 1, repeating the step A4 until all data streams are detected, and outputting a final detection result.
As a preferred technical solution of the present invention, the packet ordering method in step a2 includes the following steps:
step A2.1: by HoriginAfter the obtained channel matrix is stored as H, the channel matrix H is inverted to obtain
NtIn order to transmit the number of antennas,
for the channel inverse matrix H+The ith row vector.
Step A2.2: and respectively calculating the total amplification factor Score of the data streams in the sequencing group according to the grouping result, wherein the calculation method comprises the following steps:
the group that should be currently detected is
Step A2.3: deletion of p (th)curThe corresponding column vector of the group data stream in the channel matrix can be expressed as:
and inverting the deleted matrix to obtain a new H+. In the invention, the subtraction operation (- { }) of the matrix is defined as deleting the corresponding column in the { }.
Step A2.4: repeat steps a2.2, a2.3 until all groups are sorted.
Step A2.5: adjusting the original channel matrix H according to the sorting resultoriginSuch that the top-ranked group is first detected
As a key technical solution of the present invention, the iterative interference cancellation method in step B includes the following steps:
step B1: the packet ML algorithm is used as the initial value of detection
The superscript indicates the number of iterations.
Step B2: in parallel, the interference cancellation is performed in units of groups, and the interference cancellation process for the p-th group of data can be expressed as:
wherein
The estimated value of the ith data stream is obtained when T is 0, …, T represents the T iteration, and can be specifically represented as
Step B3: in parallel, ML detection is performed in units of groups, and the detection result of the p-th group of data can be expressed as:
step B4: the interference cancellation and detection formed by B2 and B3 is an iteration, and T ═ T +1 is repeatedly executed until a preset iteration time T ≦ T, which is usually T ≦ 5.
Next, the uplink MIMO detection method based on packet ML and parallel interference cancellation designed by the present invention is described below with reference to simulation:
simulation example one: the simulation conditions are shown in table 1 below:
table 1 simulation example a simulation condition
| Number of user-transmitted |
4 | Number of receiving antennas Nr | 32 |
| Number of |
4 | Modulation system | QPSK |
| |
0 | Number of |
5 |
| Number of data streams L in |
4 | Number of |
4 |
Fig. 3 shows a performance comparison graph of the detection algorithm proposed by the present invention, the MMSE detection algorithm and the FCSD algorithm, which are commonly used linear detection algorithms, based on the above conditions. It can be seen that the performance of the algorithm provided by the invention is better than that of the classical algorithm under the above-mentioned scenes.
Simulation example two: the simulation conditions are shown in table 2 below:
table 2 simulation example two simulation conditions
| Number of user-transmitted |
4 | Number of receiving antennas Nr | 32 |
| Number of |
4 | Modulation system | QPSK |
| Correlation coefficient ρ | 0.6 | Number of |
5 |
| Number of data streams L in |
4 | Number of |
4 |
Fig. 3 shows a performance comparison graph of the detection algorithm proposed by the present invention, the MMSE detection algorithm and the FCSD algorithm, which are commonly used linear detection algorithms, based on the above conditions. Although the performance of all detection algorithms is reduced due to the related channels, the performance of the algorithm provided by the invention is better than that of a classical algorithm under the above scenario.
The embodiments of the present invention will be described in detail with reference to the drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.
Claims (4)
1. An uplink MIMO detection method based on packet ML detection and parallel iterative interference cancellation is used for centralized uplink single-user or multi-user MIMO signal detection and is characterized in that the following steps are executed for MIMO signals to be detected obtained in real time:
step A, receiving a MIMO signal to be detected, performing grouped ML detection on the MIMO signal to be detected to obtain an initial value of parallel iterative interference cancellation, setting the initial iteration number T to be 0, setting the maximum iteration number T, and then entering step B, wherein a MIMO receiving signal y to be detected can be represented as:
wherein HiChannel, x, representing the ith useriThe base station can express the transmission data flow of the ith user, n is additive white noise, K is the total number of users, and the base station for centralized reception and detection can express the form that y is Hx + n in a concise manner according to the form of a block matrix
y＝H1x1+H2x2+…+HKxK+n＝[H1,H2,…,HK][x1,x2,…,xK]T+n＝Hx+n；
B, executing parallel interference cancellation, namely subtracting the interference of other group data streams to the group data stream to be detected, and then detecting the data stream in the group to be detected by using a grouped ML detection method, wherein the processes are executed in parallel by taking the group as a unit and are regarded as one iteration; the output of the iteration detection is used as the initial value of the parallel interference cancellation of the next iteration;
and C, skipping to execute the step B, adding 1 to the iteration time, wherein T is T +1, until the iteration time reaches the preset maximum iteration time, and the parallel iteration interference cancellation detector outputs a final detection result.
2. The uplink MIMO detection method according to claim 1, wherein the packet ML detection in step a comprises the following steps:
step A1: grouping the sending data stream and the channel matrix, wherein the number L of the data streams in the group is more than or equal to 2 and less than or equal to 4; setting the number of transmitting antennas to be NtThen, in total, can be divided into: p is NtThe group of/L is a continuous uniform group;
step A2: and B, according to the grouping result of the step A1, grouping and sorting the received data, and according to the sorting result, adjusting the column sequence of the corresponding channel matrix to be:
step A3: for the matrix adjusted in the sequence
Is subjected to QR decomposition to obtain
Obtaining the effective part y of the received vector according to the QR decomposition resulteff＝QHy, grouping the matrix R according to the grouping result of step a1 and the p-th grouping can be expressed as: rp＝[r(p-1)*L+1,r(p-1)*L+2,…,r(p-1)*L+L]，riIs the ith column vector of the matrix R;
step A4: using maximum likelihood detection in the group, the detection process of the p group of the t iteration is
CLA space formed by all possible transmitted symbol vectors generated when modulation symbols belonging to the modulation space C are transmitted in parallel on L data streams is represented; after the detection is completed, the interference of the p-th group to other data streams is cancelled, and the cancellation process can be represented as:
step A5: and (4) carrying out detection in the sequence of P, P-1, … and 1, repeating the step A4 until all data streams are detected, and outputting a final detection result.
3. The uplink MIMO detection method based on packet ML detection and parallel iterative interference cancellation according to claim 2, wherein step A2 employs a packet ordering algorithm, said packet ordering algorithm comprising the steps of;
step A2.1: by HoriginAfter the obtained channel matrix is stored as H, the channel matrix H is inverted to obtain
NtIn order to transmit the number of antennas,
for the channel inverse matrix H+The ith row vector;
step A2.2: calculating the amplification factor of the data streams in the group to be ordered separately from the grouping result as claimed in claim 2
The group that should be currently detected is
Step A2.3: deletion of p (th)curThe corresponding column vector of the group data stream in the channel matrix can be expressed as the corresponding pth in the H matrixcurGrouped column vectors
Deleting, and inverting the matrix subjected to deletion operation to obtain new H+；
Step A2.4: repeating steps a2.2, a2.3 until all groups are sorted;
step A2.5: adjusting the original channel matrix H according to the sorting resultoriginIn the order of columns of (1) to obtain
So that the top-ranked group is detected first.
4. The uplink MIMO detection method based on packet ML detection and parallel iterative interference cancellation according to claim 2, wherein the parallel iterative interference cancellation method in step B comprises the following steps:
step B1: using the packet ML algorithm of claim 2 as an initial value for detection
The superscript represents the number of iterations;
step B2: in parallel, the interference cancellation is performed in units of groups, and the interference cancellation process for the p-th group of data can be expressed as:
wherein
The estimated value of the ith data stream obtained when the T iteration is 0, … and T-1 is shown specifically as
Step B3: in parallel, ML detection is performed in units of groups, and the detection result of the p-th group of data can be expressed as:
step B4: the interference cancellation and detection composed of B2 and B3 is an iteration, and T ═ T +1 is repeatedly executed until a preset iteration time T ═ T is reached.
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